Complete Machine Learning In 6 Hours| Krish Naik

Krish Naik
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All the materials are given below https://github.com/krishnaik06/The-Grand-Complete-Data-Science-Materials/tree/main Visit https://www.krishnaik.in/liveclasses for more live classes
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Got placed for a data scientist role just after watching this video... thank you so much sir🙏
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Got placed in ml role, Thank you sir ❤
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When you draw the line on your screen and then it will automatically become straight , that is the best example of application of best fit line (linear regression)
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6hrs ago, I don't know machine learning 💀💥. Classic✨
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ALERT!!!!! For a new person who is here to explore ML and thinking whether this video is good or its just another video which will waste your time. So believe me its best ever video on youtube from a Indian. Its totally worth to watch this and make notes. From Now onwards I am a big fan of Krish Naik
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Thank you sir! Because of you I got a job as a data analyst
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The world should have so many people like you sir, your way of teaching is outstanding thank you for your time to educate the world
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Might be unpopular opinion but Krish is less boring and more coherent than Andrew Ng
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Introduction to AI vs ML vs DL vs Data Science - Explanation of AI as creating applications without human intervention. - Supervised ML focuses on regression and classification problems. 18:21 📈 Linear Regression Basics - Definition of linear regression and its purpose in modeling relationships between variables. 22:30 📉 Understanding Linear Regression Basics - Understanding intercept (theta 0) and slope (theta 1), 25:17 📊 Cost Function in Linear Regression - Definition and significance of the cost function, 34:35 📉 Impact of Theta 1 on Cost Function - Demonstrating the effect of different theta 1 values on the cost function, 41:50 🔄 Gradient Descent and Convergence Algorithm - Introduction to gradient descent as an optimization technique, 45:25 📈 Gradient Descent Basics - Understanding gradient descent in machine learning, 47:31 🏔 Dealing with Local Minima - Addressing challenges posed by local minima in gradient descent, 49:37 🔄 Iterative Convergence - Iterative convergence process in gradient descent algorithms, 55:20 📊 Performance Metrics in Linear Regression - Explaining the importance of R-squared and adjusted R-squared in evaluating model performance, 01:07:05 🔍 Overview of Regression Techniques - Introduction to ridge and lasso regression as regularization techniques, 01:09:16 📊 Understanding Overfitting and Underfitting - Understanding overfitting and underfitting in machine learning, 01:16:13 🧮 Introducing Ridge and Lasso Regression - Introducing ridge and lasso regression for regularization purposes, 01:30:56 📊 Linear Regression Assumptions and Standardization - Linear regression assumes linearity between variables and the target. 01:33:18 📈 Introduction to Logistic Regression - Logistic regression is used for binary classification tasks. 01:40:16 🎯 Understanding Logistic Regression's Decision Boundary - Logistic regression's decision boundary is determined by the sigmoid function. 01:51:28 📉 Logistic Regression Cost Function and Gradient Descent - Logistic regression cost function derivation and explanation, 01:59:06 📊 Performance Metrics: Confusion Matrix and Accuracy Calculation - Detailed explanation of the confusion matrix in binary classification, 02:03:08 ⚖ Handling Imbalanced Data in Classification - Definition and identification of imbalanced datasets in classification problems, 02:08:57 📈 Precision, Recall, and F-Score: Choosing Metrics for Different Problems - Explanation of precision and recall metrics in classification evaluation, 02:14:13 📊 Introduction to sklearn Linear Regression - Introduction to sklearn's linear regression model. 02:16:15 📈 Dataset Loading and Preparation - Loading the Boston house pricing dataset from sklearn. 02:22:08 📉 Data Splitting for Regression - Separating the dataset into independent (X) and dependent (y) features. 02:24:04 📊 Cross Validation and Mean Squared Error Calculation - Explanation of cross-validation importance in machine learning model evaluation. 02:28:31 🔄 Introduction to Ridge Regression and Hyperparameter Tuning - Introduction to Ridge Regression as a method to mitigate overfitting in linear regression. 02:34:00 📊 Ridge Regression Hyperparameter Tuning - Understanding Ridge Regression and its role in reducing overfitting, 02:37:30 📉 Impact of Hyperparameters on Model Performance - Exploring the effect of different alpha values on Ridge Regression's performance, 02:45:30 🔄 Logistic Regression for Classification - Introduction to Logistic Regression for binary classification tasks, 02:55:14 🎲 Probability Fundamentals - Probability basics: Understanding independent and dependent events. 02:56:34 📊 Conditional Probability - Explaining conditional probability using the example of drawing marbles. 02:58:12 🧮 Bayes' Theorem - Introduction to Bayes' Theorem and its significance in probability. 03:05:14 📊 Applying Probability in Classification - Applying probability concepts (e.g., conditional probability) in classification problems. 03:17:30 📊 Understanding Distance Metrics in Machine Learning - Understanding Euclidean and Manhattan distances, 03:20:18 🌳 Exploring Decision Trees for Classification and Regression - Decision tree structure and node representation, 03:24:15 🔍 Information Gain and Splitting Criteria in Decision Trees - Explaining entropy and Gini impurity as measures of impurity, 03:39:40 📊 Understanding Entropy and Information Gain - Explained the concept of entropy in decision trees and how it relates to determining pure splits. 03:41:19 📈 Using Information Gain for Feature Selection - Detailed the process of calculating information gain for different features in decision tree nodes. 03:49:17 📉 Understanding Gini Impurity vs. Entropy - Explained the concept of Gini impurity as an alternative to entropy for decision tree construction. 03:54:01 🧮 Handling Numerical Features in Decision Trees - Explored how decision trees handle continuous (numerical) features using sorted feature values. 03:59:34 ⚙ Hyperparameters in Decision Trees - Defined hyperparameters and their role in controlling decision tree complexity. 04:06:03 🌳 Decision Tree Visualization and Pruning Techniques - Understanding the structure of a decision tree through visualization. 04:09:16 🛠 Ensemble Techniques: Bagging and Boosting 04:21:31 🌲 Random Forest Classifier and Regressor - Solving overfitting in decision trees through ensemble learning. 04:24:20 🌳 Random Forest: Overview and Working - Random Forest combines multiple decision trees to create a generalized model with low bias and low variance. - Combines predictions from multiple decision trees (ensemble method). - Uses bootstrapping and feature sampling to train each tree on different subsets of data. - Prevents overfitting present in individual decision trees. 04:29:27 🚀 Boosting Techniques: Introduction to Adaboost - Adaboost is a boosting technique that sequentially combines weak learners to form a strong learner. - Begins by assigning equal weights to all training examples. - Focuses on correcting misclassified examples in subsequent models. - Uses weighted voting to combine outputs of weak learners into a final prediction. 04:42:27 📊 Adaboost: Training Process and Weight Update - Adaboost updates weights of training examples based on the performance of each weak learner. - Calculates the total error of each weak learner to determine performance. - Adjusts weights of training examples to emphasize incorrectly classified instances. - Normalizes weights to ensure they sum up to 1 for the next iteration of training. 04:45:24 🌲 Decision between Black Box and White Box Models - Decision trees are considered white box models because their splits are visible and interpretable. 04:47:15 🎯 Introduction to K-means Clustering - K-means clustering is an unsupervised learning method used to group similar data points together. 04:50:00 📊 Understanding Centroids in K-means - Centroids in K-means represent the center of each cluster and are initially placed randomly. 04:56:31 📉 Determining Optimal K in K-means Clustering - The elbow method is used to determine the optimal number of clusters (k) by plotting within-cluster sum of squares (WCSS) against different k values. 05:05:22 🌐 Hierarchical Clustering Overview - Understanding hierarchical clustering involves identifying clusters based on the longest vertical lines without horizontal intersections. 05:07:30 🕰 Time Complexity in Clustering Algorithms - Hierarchical clustering generally takes longer with large datasets due to dendrogram construction, compared to faster performance by k-means. 05:09:04 📊 Validating Clustering Models - For clustering validation, methods like silhouette scores are crucial, quantifying cluster quality. 05:17:21 🌌 DBSCAN Clustering Essentials - DBSCAN identifies core points, border points, and noise points based on defined parameters like epsilon and min points. 05:26:37 📊 Exploring K-Means Clustering and Silhouette Score - Explains the process of using K-Means clustering and evaluating it with silhouette scores. 05:35:30 🧠 Understanding Bias and Variance - Defines bias as a phenomenon influencing algorithm results towards or against a specific idea or training data. 05:48:51 🌳 Decision Tree Construction - Understanding binary decision tree creation in XGBoost, 05:51:39 📊 Similarity Weight Calculation 05:57:22 📈 Information Gain Computation 06:05:01 🚀 XGBoost Classifier Inference Process 06:09:39 🌳 Decision Tree - Splitting Based on Experience 06:11:31 📊 Calculation of Similarity Weight and Information Gain 06:18:59 🌳 Regression Tree - Inference and Output 06:26:24 🚀 SVM - Marginal Planes and Hyperplanes 06:30:51 📈 SVM Margin Maximization 06:31:34 🛠 SVM Optimization Objectives 06:32:29 🔍 SVM Decision Boundary Clarity
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5:38:40 little correction the model performed well with training data; it has low bias and model performed poor with training data; it has high bias
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04:08:29 The Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the subset. The Gini impurity can be computed by summing the probability of each item being chosen times the probability of a mistake in categorizing that item. It reaches its minimum (zero) when all cases in the node fall into a single target category. In the case of the Iris dataset, the root node contains all the instances, and if they are evenly distributed among the three classes (setosa, versicolor, virginica), the Gini impurity will be 0.667. This is because the probability of choosing an instance from any class is 1/3, and the probability of misclassifying it is 2/3 (since there are two other classes). The calculation is as follows: Gini Impurity = 1 - (1/3)^2 - (1/3)^2 - (1/3)^2 = 0.667 This indicates that there is a 66.7% chance of misclassifying a randomly chosen element from the dataset if it was labeled according to the distribution of labels in the entire dataset. The code you provided is plotting the decision tree. The Gini impurity for each node is calculated during the creation of the decision tree, not during the plotting. The Gini impurity is shown on the plot for each node.
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5:38:30 youre interchanging the definition of high bias and low bias
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🎯 Key points for quick navigation: 00:48 🤖 Introduction to AI vs ML vs DL vs Data Science - Explanation of AI as creating applications without human intervention. - Supervised ML focuses on regression and classification problems. 18:21 📈 Linear Regression Basics - Definition of linear regression and its purpose in modeling relationships between variables. 22:30 📉 Understanding Linear Regression Basics - Understanding intercept (theta 0) and slope (theta 1), 25:17 📊 Cost Function in Linear Regression - Definition and significance of the cost function, 34:35 📉 Impact of Theta 1 on Cost Function - Demonstrating the effect of different theta 1 values on the cost function, 41:50 🔄 Gradient Descent and Convergence Algorithm - Introduction to gradient descent as an optimization technique, 45:25 📈 Gradient Descent Basics - Understanding gradient descent in machine learning, 47:31 🏔️ Dealing with Local Minima - Addressing challenges posed by local minima in gradient descent, 49:37 🔄 Iterative Convergence - Iterative convergence process in gradient descent algorithms, 55:20 📊 Performance Metrics in Linear Regression - Explaining the importance of R-squared and adjusted R-squared in evaluating model performance, 01:07:05 🔍 Overview of Regression Techniques - Introduction to ridge and lasso regression as regularization techniques, 01:09:16 📊 Understanding Overfitting and Underfitting - Understanding overfitting and underfitting in machine learning, 01:16:13 🧮 Introducing Ridge and Lasso Regression - Introducing ridge and lasso regression for regularization purposes, 01:30:56 📊 Linear Regression Assumptions and Standardization - Linear regression assumes linearity between variables and the target. 01:33:18 📈 Introduction to Logistic Regression - Logistic regression is used for binary classification tasks. 01:40:16 🎯 Understanding Logistic Regression's Decision Boundary - Logistic regression's decision boundary is determined by the sigmoid function. 01:51:28 📉 Logistic Regression Cost Function and Gradient Descent - Logistic regression cost function derivation and explanation, 01:59:06 📊 Performance Metrics: Confusion Matrix and Accuracy Calculation - Detailed explanation of the confusion matrix in binary classification, 02:03:08 ⚖️ Handling Imbalanced Data in Classification - Definition and identification of imbalanced datasets in classification problems, 02:08:57 📈 Precision, Recall, and F-Score: Choosing Metrics for Different Problems - Explanation of precision and recall metrics in classification evaluation, 02:14:13 📊 Introduction to sklearn Linear Regression - Introduction to sklearn's linear regression model. 02:16:15 📈 Dataset Loading and Preparation - Loading the Boston house pricing dataset from sklearn. 02:22:08 📉 Data Splitting for Regression - Separating the dataset into independent (X) and dependent (y) features. 02:24:04 📊 Cross Validation and Mean Squared Error Calculation - Explanation of cross-validation importance in machine learning model evaluation. 02:28:31 🔄 Introduction to Ridge Regression and Hyperparameter Tuning - Introduction to Ridge Regression as a method to mitigate overfitting in linear regression. 02:34:00 📊 Ridge Regression Hyperparameter Tuning - Understanding Ridge Regression and its role in reducing overfitting, 02:37:30 📉 Impact of Hyperparameters on Model Performance - Exploring the effect of different alpha values on Ridge Regression's performance, 02:45:30 🔄 Logistic Regression for Classification - Introduction to Logistic Regression for binary classification tasks, 02:55:14 🎲 Probability Fundamentals - Probability basics: Understanding independent and dependent events. 02:56:34 📊 Conditional Probability - Explaining conditional probability using the example of drawing marbles. 02:58:12 🧮 Bayes' Theorem - Introduction to Bayes' Theorem and its significance in probability. 03:05:14 📊 Applying Probability in Classification - Applying probability concepts (e.g., conditional probability) in classification problems. 03:17:30 📊 Understanding Distance Metrics in Machine Learning - Understanding Euclidean and Manhattan distances, 03:20:18 🌳 Exploring Decision Trees for Classification and Regression - Decision tree structure and node representation, 03:24:15 🔍 Information Gain and Splitting Criteria in Decision Trees - Explaining entropy and Gini impurity as measures of impurity, 03:39:40 📊 Understanding Entropy and Information Gain - Explained the concept of entropy in decision trees and how it relates to determining pure splits. 03:41:19 📈 Using Information Gain for Feature Selection - Detailed the process of calculating information gain for different features in decision tree nodes. 03:49:17 📉 Understanding Gini Impurity vs. Entropy - Explained the concept of Gini impurity as an alternative to entropy for decision tree construction. 03:54:01 🧮 Handling Numerical Features in Decision Trees - Explored how decision trees handle continuous (numerical) features using sorted feature values. 03:59:34 ⚙️ Hyperparameters in Decision Trees - Defined hyperparameters and their role in controlling decision tree complexity. 04:06:03 🌳 Decision Tree Visualization and Pruning Techniques - Understanding the structure of a decision tree through visualization. 04:09:16 🛠️ Ensemble Techniques: Bagging and Boosting 04:21:31 🌲 Random Forest Classifier and Regressor - Solving overfitting in decision trees through ensemble learning. 04:24:20 🌳 Random Forest: Overview and Working - Random Forest combines multiple decision trees to create a generalized model with low bias and low variance. - Combines predictions from multiple decision trees (ensemble method). - Uses bootstrapping and feature sampling to train each tree on different subsets of data. - Prevents overfitting present in individual decision trees. 04:29:27 🚀 Boosting Techniques: Introduction to Adaboost - Adaboost is a boosting technique that sequentially combines weak learners to form a strong learner. - Begins by assigning equal weights to all training examples. - Focuses on correcting misclassified examples in subsequent models. - Uses weighted voting to combine outputs of weak learners into a final prediction. 04:42:27 📊 Adaboost: Training Process and Weight Update - Adaboost updates weights of training examples based on the performance of each weak learner. - Calculates the total error of each weak learner to determine performance. - Adjusts weights of training examples to emphasize incorrectly classified instances. - Normalizes weights to ensure they sum up to 1 for the next iteration of training. 04:45:24 🌲 Decision between Black Box and White Box Models - Decision trees are considered white box models because their splits are visible and interpretable. 04:47:15 🎯 Introduction to K-means Clustering - K-means clustering is an unsupervised learning method used to group similar data points together. 04:50:00 📊 Understanding Centroids in K-means - Centroids in K-means represent the center of each cluster and are initially placed randomly. 04:56:31 📉 Determining Optimal K in K-means Clustering - The elbow method is used to determine the optimal number of clusters (k) by plotting within-cluster sum of squares (WCSS) against different k values. 05:05:22 🌐 Hierarchical Clustering Overview - Understanding hierarchical clustering involves identifying clusters based on the longest vertical lines without horizontal intersections. 05:07:30 🕰️ Time Complexity in Clustering Algorithms - Hierarchical clustering generally takes longer with large datasets due to dendrogram construction, compared to faster performance by k-means. 05:09:04 📊 Validating Clustering Models - For clustering validation, methods like silhouette scores are crucial, quantifying cluster quality. 05:17:21 🌌 DBSCAN Clustering Essentials - DBSCAN identifies core points, border points, and noise points based on defined parameters like epsilon and min points. 05:26:37 📊 Exploring K-Means Clustering and Silhouette Score - Explains the process of using K-Means clustering and evaluating it with silhouette scores. 05:35:30 🧠 Understanding Bias and Variance - Defines bias as a phenomenon influencing algorithm results towards or against a specific idea or training data. 05:48:51 🌳 Decision Tree Construction - Understanding binary decision tree creation in XGBoost, 05:51:39 📊 Similarity Weight Calculation 05:57:22 📈 Information Gain Computation 06:05:01 🚀 XGBoost Classifier Inference Process 06:09:39 🌳 Decision Tree - Splitting Based on Experience 06:11:31 📊 Calculation of Similarity Weight and Information Gain 06:18:59 🌳 Regression Tree - Inference and Output 06:26:24 🚀 SVM - Marginal Planes and Hyperplanes 06:30:51 📈 SVM Margin Maximization 06:31:34 🛠️ SVM Optimization Objectives 06:32:29 🔍 SVM Decision Boundary Clarity Made with HARPA AI
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I'm amazed by seeing your understanding with every algorithm👏👏. one day I'll also be able to do the same.
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One of the best ML videos available in the internet. This video is crisp yet covers most of the topics of ML.. Also I like the way Krish explains theory part first and then explains the same using practical examples.
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52:00 The partial derivative for Theta 0(bias) is 1/m sigma (i=1 to n)(y(hat)-y) there will be no square after doing partial derivative
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Not recommended for beginners, but if you already have some knowledge and wants to revise concepts this is the best video. very clear and concise explation
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Please check at 1:18:48 there is a calculation mistake. 0+1(2) = 3?
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the way you teach is cake wake coaching... even a ground scratching beginner can shine in DS if they watch all your Video... Thank you!!!
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